SFARI Genes for All Lobes but Frontal

#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')

library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(plotlyutils)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(Rtsne)
library(ClusterR)
library(DESeq2)
library(expss)
library(knitr)

Load preprocessed dataset (preprocessing code in 20_03_02_data_preprocessing.Rmd)

# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame

# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>% 
              mutate('ID'=as.character(ensembl_gene_id)) %>% 
              dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
              mutate('Neuronal'=1)

# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_with_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]

# Update DE_info with SFARI and Neuronal information
DE_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
  mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
  distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
  mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
  mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`), significant=padj<0.05 & !is.na(padj))


SFARI_colour_hue = function(r) {
  pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}

---

SFARI Gene list

cat(paste0('There are ', length(unique(SFARI_genes$`gene-symbol`)), ' genes with a SFARI score'))

## There are 979 genes with a SFARI score

The results from this section don’t change depending on the brain region analised, so I’m not going to repeat them here. These can be found in the folder where all the brain regions were analysed together

---

Exploratory Analysis

As in the previous section, the results from this section don’t change depending on the brain region analised, so I’m not going to repeat them here. These can be found in the folder where all the brain regions were analysed together

Gene Expression

Normalised data

• The higher the SFARI score, the higher the mean expression of the gene: This pattern is quite strong and it doesn’t have any biological interpretation, so it’s probably bias in the SFARI score assignment

• The higher the SFARI score, the lower the standard deviation: This pattern is not as strong, but it is weird because the data was originally heteroscedastic with a positive relation between mean and variance, so the fact that the relation now seems to have reversed could mean that the vst normalisation ended up affecting the highly expressed genes more than it should have when trying to correct their higher variance

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>% 
            left_join(DE_info, by='ID')

p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              theme(legend.position='none'))

p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              ggtitle('Mean Expression (left) and SD (right) by SFARI score') + 
              theme(legend.position='none'))

subplot(p1, p2, nrows=1)

rm(plot_data, p1, p2)

Raw data

Just to corroborate that the relation between sd and SFARI score used to be in the opposite direction before the normalisation: The higher the SFARI score the higher the mean expression and the higher the standard deviation

*There are a lot of outliers, but the plot is interactive so you can zoom in

# Save preprocessed results
datExpr_prep = datExpr
datMeta_prep = datMeta
DE_info_prep = DE_info

load('./../Data/filtered_raw_data.RData')

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>% 
            left_join(DE_info, by='ID')

p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              theme(legend.position='none'))

p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              ggtitle('Mean Expression (left) and SD (right) by SFARI score') + 
              theme(legend.position='none'))

subplot(p1, p2, nrows=1)

rm(plot_data, p1, p2)

Return to normalised version of the data

# Save preprocessed results
datExpr = datExpr_prep
datMeta = datMeta_prep
DE_info = DE_info_prep

rm(datExpr_prep, datMeta_prep, DE_info_prep)

Log Fold Change

There seems to be a negative relation between SFARI score and log fold change when it would be expected to be either positively correlated or independent from each other (this last one because there are other factors that determine if a gene is releated to Autism apart from differences in gene expression)

Wikipedia mentions the likely explanation for this: “A disadvantage and serious risk of using fold change in this setting is that it is biased and may misclassify differentially expressed genes with large differences (B − A) but small ratios (B/A), leading to poor identification of changes at high expression levels”.

Based on this, since we saw there is a strong relation between SFARI score and mean expression, the bias in log fold change affects mainly genes with high SFARI scores, which would be the ones we are most interested in.

On top of this, I believe this effect is made more extreme by the pattern found in the previous plots, since the higher expressed genes were the most affected by the normalisation transformation, ending up with a smaller variance than the rest of the data, which is related to smaller ratios. (This is a constant problem independently of the normalisation function used).

ggplotly(DE_info %>% ggplot(aes(x=gene.score, y=abs(log2FoldChange), fill=gene.score)) + 
         geom_boxplot() + scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + 
         theme_minimal() + theme(legend.position='none'))

Effects of modifying filtering threshold by SFARI score

• The behaviour is very similar to the one found when considering all of the brain regions

• All SFARI scores except 6 are more affected by the filtering than the genes with Neuronal-related functional annotations

• SFARI score 1 is the one with the consistently lowest percentage of remaining genes

• Using the null hypothesis \(H0: lfc=0\), 225/899 SFARI genes are statistically significant (~25%)

lfc_list = seq(1, 1.3, 0.01)

all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_info)))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_info$Neuronal)))
lfc_counts_all = DE_info %>% group_by(`gene-score`) %>% tally %>%
                 mutate('group'=as.factor(`gene-score`), 'n'=as.character(n)) %>%
                 dplyr::select(group, n) %>%
                 bind_rows(Neuronal_counts, all_counts) %>%
                 mutate('lfc'=-1) %>%  dplyr::select(lfc, group, n)

for(lfc in lfc_list){
  
  # Recalculate DE_info with the new threshold (p-values change)
  DE_genes = results(dds, lfcThreshold=log2(lfc), altHypothesis='greaterAbs') %>% data.frame
  
  DE_genes = DE_genes %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
             mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
             distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
             mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
             mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`))
  
  DE_genes = DE_genes %>% filter(padj<0.05 & abs(log2FoldChange)>log2(lfc))

  
  # Calculate counts by groups
  all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
  Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Neuronal)))
  lfc_counts = DE_genes %>% group_by(`gene-score`) %>% tally %>%
               mutate('group'=`gene-score`, 'n'=as.character(n)) %>%
               bind_rows(Neuronal_counts, all_counts) %>%
               mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
  
  
  # Update lfc_counts_all
  lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}

# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>% 
  left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)

# Calculate percentage of each group remaining
tot_counts = DE_info %>% group_by(`gene-score`) %>% tally() %>% filter(`gene-score`!='None') %>%
             mutate('group'=`gene-score`, 'tot'=n) %>% dplyr::select(group, tot) %>%
             bind_rows(data.frame('group'='Neuronal', 'tot'=sum(DE_info$Neuronal)),
                       data.frame('group'='All', 'tot'=nrow(DE_info)))

lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1, group!='None') %>% 
                 left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))


# Plot change of number of genes
ggplotly(lfc_counts_all %>% ggplot(aes(lfc, perc, color=group)) + geom_point(aes(id=n)) + geom_line() + 
         scale_color_manual(values=SFARI_colour_hue(r=1:8)) + ylab('% of remaining genes') +  xlab('Fold Change') + 
         ggtitle('Effect of filtering thresholds by SFARI score') + theme_minimal())

rm(lfc_list, all_counts, Neuronal_counts, lfc_counts_all, lfc, lfc_counts, lfc_counts_all, tot_counts, lfc_counts_all)

cat(paste0('There are ', sum(DE_info$padj<0.05 & DE_info$`gene-score` != 'None' & !is.na(DE_info$padj)),
           ' SFARI genes that are differentially expressed'))

## There are 225 SFARI genes that are differentially expressed

kable(DE_info %>% filter(padj<0.05 & `gene-score` %in% c(1,2,3) & !is.na(padj)) %>% 
      dplyr::select(ID, `gene-symbol`, log2FoldChange, padj, `gene-score`, Neuronal) %>% arrange(`gene-score`,padj),
      caption = 'Top SFARI scores that are DE')

Top SFARI scores that are DE

ID	gene-symbol	log2FoldChange	padj	gene-score	Neuronal
ENSG00000136535	TBR1	-0.4554099	0.0005916	1	1
ENSG00000141431	ASXL3	0.1625706	0.0039926	1	0
ENSG00000110066	KMT5B	0.1275986	0.0051519	1	0
ENSG00000108510	MED13	0.1588882	0.0000695	2	0
ENSG00000055609	KMT2C	0.2200318	0.0003362	2	0
ENSG00000174469	CNTNAP2	-0.3471929	0.0005424	2	1
ENSG00000038382	TRIO	0.1317753	0.0006398	2	0
ENSG00000169432	SCN9A	0.5126869	0.0013166	2	1
ENSG00000147050	KDM6A	0.1807835	0.0022758	2	0
ENSG00000157103	SLC6A1	-0.1929282	0.0057995	2	1
ENSG00000118482	PHF3	0.1460686	0.0067777	2	0
ENSG00000061676	NCKAP1	-0.1376989	0.0079805	2	0
ENSG00000117139	KDM5B	0.1249817	0.0102658	2	0
ENSG00000155511	GRIA1	0.1719235	0.0117624	2	1
ENSG00000165186	PTCHD1	0.3381020	0.0121792	2	0
ENSG00000123066	MED13L	0.1110681	0.0142652	2	0
ENSG00000155974	GRIP1	-0.2257287	0.0154464	2	1
ENSG00000102786	INTS6	0.1352041	0.0324529	2	0
ENSG00000119866	BCL11A	-0.2189133	0.0342732	2	1
ENSG00000139613	SMARCC2	-0.1223736	0.0460425	2	0
ENSG00000114166	KAT2B	0.1532367	0.0484852	2	0
ENSG00000196876	SCN8A	-0.3411377	0.0000026	3	1
ENSG00000181722	ZBTB20	0.2855065	0.0000060	3	0
ENSG00000144285	SCN1A	-0.5480609	0.0000280	3	1
ENSG00000132294	EFR3A	-0.2355612	0.0000327	3	0
ENSG00000182621	PLCB1	-0.3009944	0.0000352	3	0
ENSG00000116117	PARD3B	0.3240212	0.0000448	3	0
ENSG00000181090	EHMT1	0.1826603	0.0000565	3	0
ENSG00000078328	RBFOX1	-0.3213438	0.0000626	3	0
ENSG00000157087	ATP2B2	-0.3796322	0.0000626	3	1
ENSG00000136854	STXBP1	-0.3632598	0.0000909	3	1
ENSG00000074590	NUAK1	-0.2942085	0.0002871	3	0
ENSG00000005955	GGNBP2	-0.1479284	0.0003671	3	0
ENSG00000135046	ANXA1	0.7229080	0.0004908	3	0
ENSG00000170579	DLGAP1	-0.2473525	0.0006198	3	0
ENSG00000141646	SMAD4	0.2237325	0.0007865	3	1
ENSG00000003147	ICA1	-0.2710361	0.0014503	3	0
ENSG00000168769	TET2	0.2062366	0.0018709	3	0
ENSG00000197535	MYO5A	-0.1902305	0.0020773	3	1
ENSG00000175497	DPP10	-0.1791317	0.0026958	3	0
ENSG00000171759	PAH	-0.5590091	0.0028407	3	0
ENSG00000132604	TERF2	-0.1500059	0.0032574	3	0
ENSG00000109911	ELP4	0.2259065	0.0038051	3	0
ENSG00000124140	SLC12A5	-0.2575052	0.0041292	3	1
ENSG00000079482	OPHN1	0.1865265	0.0047777	3	0
ENSG00000083168	KAT6A	0.1091441	0.0049672	3	0
ENSG00000107077	KDM4C	0.1795803	0.0051551	3	0
ENSG00000162946	DISC1	0.2708303	0.0051589	3	1
ENSG00000104388	RAB2A	-0.0944913	0.0070838	3	0
ENSG00000113328	CCNG1	0.1629147	0.0084205	3	1
ENSG00000166313	APBB1	-0.1627624	0.0109047	3	1
ENSG00000065526	SPEN	0.2035973	0.0112258	3	0
ENSG00000127616	SMARCA4	-0.1296403	0.0140253	3	0
ENSG00000183454	GRIN2A	-0.2257254	0.0143137	3	1
ENSG00000152583	SPARCL1	-0.2658609	0.0147818	3	0
ENSG00000170745	KCNS3	-0.3819937	0.0175682	3	0
ENSG00000185345	PRKN	-0.1278119	0.0175682	3	1
ENSG00000197102	DYNC1H1	-0.1170236	0.0192866	3	0
ENSG00000103197	TSC2	-0.0889704	0.0207997	3	1
ENSG00000139726	DENR	-0.0909958	0.0211868	3	0
ENSG00000205581	HMGN1	0.1688763	0.0218656	3	0
ENSG00000127124	HIVEP3	0.2199833	0.0241009	3	0
ENSG00000182771	GRID1	-0.1379088	0.0246139	3	0
ENSG00000166501	PRKCB	-0.2101262	0.0250970	3	0
ENSG00000127914	AKAP9	0.1628572	0.0252475	3	0
ENSG00000106290	TAF6	-0.1701427	0.0253658	3	0
ENSG00000142230	SAE1	-0.1064167	0.0255657	3	0
ENSG00000021645	NRXN3	-0.1552705	0.0288066	3	0
ENSG00000072682	P4HA2	0.2348559	0.0325144	3	0
ENSG00000107099	DOCK8	0.2730997	0.0351375	3	0
ENSG00000176884	GRIN1	-0.1887357	0.0387815	3	1
ENSG00000158321	AUTS2	0.1485577	0.0419493	3	0
ENSG00000128815	WDFY4	0.2626927	0.0460539	3	0
ENSG00000146247	PHIP	0.1161604	0.0462047	3	0
ENSG00000163637	PRICKLE2	0.1421890	0.0491054	3	1

---

Session info

sessionInfo()

## R version 3.6.2 (2019-12-12)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] knitr_1.23                  expss_0.10.1               
##  [3] DESeq2_1.24.0               SummarizedExperiment_1.14.1
##  [5] DelayedArray_0.10.0         BiocParallel_1.18.1        
##  [7] matrixStats_0.54.0          Biobase_2.44.0             
##  [9] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
## [11] IRanges_2.18.2              S4Vectors_0.22.0           
## [13] BiocGenerics_0.30.0         ClusterR_1.2.1             
## [15] gtools_3.8.1                Rtsne_0.15                 
## [17] GGally_1.4.0                gridExtra_2.3              
## [19] viridis_0.5.1               viridisLite_0.3.0          
## [21] RColorBrewer_1.1-2          plotlyutils_0.0.0.9000     
## [23] plotly_4.9.1                glue_1.3.1                 
## [25] reshape2_1.4.3              forcats_0.4.0              
## [27] stringr_1.4.0               dplyr_0.8.2                
## [29] purrr_0.3.3                 readr_1.3.1                
## [31] tidyr_0.8.3                 tibble_2.1.3               
## [33] ggplot2_3.2.0               tidyverse_1.2.1            
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1       htmlTable_1.13.1       XVector_0.24.0        
##  [4] base64enc_0.1-3        rstudioapi_0.10        bit64_0.9-7           
##  [7] AnnotationDbi_1.46.1   lubridate_1.7.4        xml2_1.2.2            
## [10] splines_3.6.2          geneplotter_1.62.0     zeallot_0.1.0         
## [13] Formula_1.2-3          jsonlite_1.6           broom_0.5.2           
## [16] annotate_1.62.0        cluster_2.1.0          shiny_1.3.2           
## [19] compiler_3.6.2         httr_1.4.0             backports_1.1.4       
## [22] assertthat_0.2.1       Matrix_1.2-18          lazyeval_0.2.2        
## [25] cli_1.1.0              later_0.8.0            acepack_1.4.1         
## [28] htmltools_0.3.6        tools_3.6.2            gmp_0.5-13.5          
## [31] gtable_0.3.0           GenomeInfoDbData_1.2.1 Rcpp_1.0.1            
## [34] cellranger_1.1.0       vctrs_0.2.0            nlme_3.1-144          
## [37] crosstalk_1.0.0        xfun_0.8               rvest_0.3.4           
## [40] mime_0.7               XML_3.98-1.20          zlibbioc_1.30.0       
## [43] scales_1.0.0           promises_1.0.1         hms_0.5.1             
## [46] yaml_2.2.0             memoise_1.1.0          rpart_4.1-15          
## [49] reshape_0.8.8          latticeExtra_0.6-28    stringi_1.4.3         
## [52] RSQLite_2.1.2          highr_0.8              genefilter_1.66.0     
## [55] checkmate_1.9.4        rlang_0.4.2            pkgconfig_2.0.2       
## [58] bitops_1.0-6           evaluate_0.14          lattice_0.20-40       
## [61] labeling_0.3           htmlwidgets_1.3        bit_1.1-14            
## [64] tidyselect_0.2.5       plyr_1.8.4             magrittr_1.5          
## [67] R6_2.4.0               generics_0.0.2         Hmisc_4.2-0           
## [70] DBI_1.0.0              pillar_1.4.2           haven_2.1.1           
## [73] foreign_0.8-75         withr_2.1.2            survival_3.1-8        
## [76] RCurl_1.95-4.12        nnet_7.3-12            modelr_0.1.5          
## [79] crayon_1.3.4           rmarkdown_1.13         locfit_1.5-9.1        
## [82] grid_3.6.2             readxl_1.3.1           data.table_1.12.2     
## [85] blob_1.2.0             digest_0.6.19          xtable_1.8-4          
## [88] httpuv_1.5.1           munsell_0.5.0